---
title: Wan2.2
metatags:
    description: "Deploy Wan2.2 video generation models with SGLang - MoE architecture, cinematic aesthetics, and efficient 720P@24fps generation."
---

import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
import { Wan22Deployment } from '/src/snippets/diffusion/wan22-deployment.jsx';

<DiffusionModelTags tags={["video", "text-to-video", "image-to-video", "TI2V", "MoE"]} />

## 1. Model Introduction

[Wan2.2 series](https://github.com/Wan-Video/Wan2.2) are the most popular and open and advanced large-scale video generative models.

This generation delivers comprehensive upgrades across the board:

- **Effective MoE Architecture**: Introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- **Cinematic-level Aesthetics**: Incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
- **Complex Motion Generation**: Trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
- **Efficient High-Definition Hybrid TI2V**: Open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously.

For more details, please refer to the [official Wan2.2 GitHub Repository](https://github.com/Wan-Video/Wan2.2).

## 2. SGLang-diffusion Installation

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the [official SGLang-diffusion installation guide](https://docs.sglang.io/docs/sglang-diffusion/installation) for installation instructions.

## 3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

### 3.1 Basic Configuration

The Wan2.2 series offers models in various sizes, architectures and input types, optimized for different hardware platforms. The recommended launch configurations vary by hardware and model size.

**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, model size. SGLang supports serving Wan2.2 on NVIDIA B200, H200, AMD MI300X, MI325X, MI355X GPUs and Ascend A2, A3 NPUs.

<Wan22Deployment />

### 3.2 Configuration Tips

Currently supported optimizations are listed [here](/docs/sglang-diffusion/compatibility_matrix).

- `--vae-path`: Path to a custom VAE model or HuggingFace model ID (e.g., fal/FLUX.2-Tiny-AutoEncoder). If not specified, the VAE will be loaded from the main model path.
- `--num-gpus {NUM_GPUS}`: Number of GPUs to use
- `--tp-size {TP_SIZE}`: Tensor parallelism size (only for the encoder; should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
- `--sp-degree {SP_SIZE}`: Sequence parallelism size (typically should match the number of GPUs)
- `--ulysses-degree {ULYSSES_DEGREE}`: The degree of DeepSpeed-Ulysses-style SP in USP
- `--ring-degree {RING_DEGREE}`: The degree of ring attention-style SP in USP

## 4. Model Invocation

### 4.1 Basic Usage

For more API usage and request examples, please refer to:
[SGLang Diffusion OpenAI API](/docs/sglang-diffusion/api/openai_api)

#### 4.1.1 Launch a server and then send requests

```shell Command
sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers --port 3000

curl http://127.0.0.1:3000/v1/images/generations \
  -o >(jq -r '.data[0].b64_json' | base64 --decode > example.png) \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
    "prompt": "A cute baby sea otter",
    "n": 1,
    "size": "1024x1024",
    "response_format": "b64_json"
  }'
```

#### 4.1.2 Generate a video without launching a server

```shell Command
SERVER_ARGS=(
  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
  --text-encoder-cpu-offload
  --pin-cpu-memory
  --num-gpus 4
  --ulysses-degree=2
  --enable-cfg-parallel
)

SAMPLING_ARGS=(
  --prompt "A curious raccoon"
  --save-output
  --output-path outputs
  --output-file-name "A curious raccoon.mp4"
)

sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"

```

### 4.2 Advanced Usage

#### 4.2.1 Cache-DiT Acceleration

SGLang integrates [Cache-DiT](https://github.com/vipshop/cache-dit), a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to 7.4x inference speedup with minimal quality loss. You can set `SGLANG_CACHE_DIT_ENABLED=True` to enable it. For more details, please refer to the SGLang Cache-DiT [documentation](/docs/sglang-diffusion/cache_dit).

**Basic Usage**

```shell Command
SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
```

**Advanced Usage**

- DBCache Parameters: DBCache controls block-level caching behavior:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Fn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_FN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of first blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Bn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_BN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of last blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>W</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_WARMUP`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Warmup steps before caching starts</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>R</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_RDT`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.24</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Residual difference threshold</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MC</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_MC`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>3</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Maximum continuous cached steps</td>
    </tr>
  </tbody>
</table>

- TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Enable</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TAYLORSEER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>false</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Enable TaylorSeer calibrator</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Order</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TS_ORDER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Taylor expansion order (1 or 2)</td>
    </tr>
  </tbody>
</table>

  Combined Configuration Example:

```shell Command
SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=2 \
SGLANG_CACHE_DIT_BN=1 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.4 \
SGLANG_CACHE_DIT_MC=4 \
SGLANG_CACHE_DIT_TAYLORSEER=true \
SGLANG_CACHE_DIT_TS_ORDER=2 \
sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
```

#### 4.2.2 GPU Optimization

- `--dit-cpu-offload`: Use CPU offload for DiT inference. Enable if run out of memory with FSDP.
- `--text-encoder-cpu-offload`: Use CPU offload for text encoder inference. Enable if run out of memory with FSDP.
- `--image-encoder-cpu-offload`: Use CPU offload for image encoder inference. Enable if run out of memory with FSDP.
- `--vae-cpu-offload`: Use CPU offload for VAE. Enable if run out of memory.
- `--pin-cpu-memory`: Pin memory for CPU offload. Only added as a temp workaround if it throws "CUDA error: invalid argument".

#### 4.2.3 Supported LoRA Registry
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "50%"}} />
    <col style={{width: "50%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>origin model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>supported LoRA</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[Wan-AI/Wan2.2-I2V-A14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>[lightx2v/Wan2.2-Distill-Loras](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[Wan-AI/Wan2.2-T2V-A14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>[Cseti/wan2.2-14B-Arcane_Jinx-lora-v1](https://huggingface.co/Cseti/wan2.2-14B-Arcane_Jinx-lora-v1)</td>
    </tr>
  </tbody>
</table>
**Example**:
```shell Command
sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers --port 3000 \
    --lora-path Cseti/wan2.2-14B-Arcane_Jinx-lora-v1
```

## 5. Benchmark

Test Environment:

- Hardware: NVIDIA B200 GPU (1x)
- Model: Wan-AI/Wan2.2-T2V-A14B-Diffusers
- sglang diffusion version: 0.5.6.post2

### 5.1 Speedup Benchmark

### 5.1.1 Generate a video

<Tabs>
  <Tab title="NVIDIA B200">
    **Server Command**:
    ```shell Command
    sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
    ```

    **Benchmark Command**:
    ```shell Command
    python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
        --backend sglang-video --dataset vbench --task t2v --num-prompts 1 --max-concurrency 1
    ```

    **Result**:
    ```text Output
    ================= Serving Benchmark Result =================
    Backend:                                 sglang-video
    Model:                                   Wan-AI/Wan2.2-T2V-A14B-Diffusers
    Dataset:                                 vbench
    Task:                                    t2v
    --------------------------------------------------
    Benchmark duration (s):                  630.43
    Request rate:                            inf
    Max request concurrency:                 1
    Successful requests:                     1/1
    --------------------------------------------------
    Request throughput (req/s):              0.00
    Latency Mean (s):                        630.4277
    Latency Median (s):                      630.4277
    Latency P99 (s):                         630.4277
    --------------------------------------------------
    Peak Memory Max (MB):                    62627.41
    Peak Memory Mean (MB):                   62627.41
    Peak Memory Median (MB):                 62627.41

    ============================================================
    ```
  </Tab>

  <Tab title="Ascend A3">
    **Server Command**:
    ```shell Command
    #One A3 card has 2 npu chips. Using four A3 cards in benchmarking
    sglang serve \
      --model-path /models/Wan-AI/Wan2.2-T2V-A14B-Diffusers/ \
      --tp-size 2 \
      --sp-degree 4 \
      --num-gpus 8 \
      --attention-backend laser_attn
    ```

    **Benchmark Command**:
    ```shell Command
    python -m sglang.multimodal_gen.benchmarks.bench_serving \
      --dataset vbench \
      --task text-to-video \
      --num-prompts 1 \
      --max-concurrency 1
    ```

    **Result**:
    ```text Output
    ================= Serving Benchmark Result =================
    Task:                                         text-to-video
    Model:                                        Wan-AI/Wan2.2-T2V-A14B-Diffusers/
    Dataset:                                      vbench
    --------------------------------------------------
    Benchmark duration (s):                       214.50
    Request rate:                                 inf
    Max request concurrency:                      1
    Successful requests:                          1/1
    Completed outputs:                            1
    Outputs per prompt:                           1
    --------------------------------------------------
    Request throughput (req/s):                   0.00
    Output throughput (outputs/s):                0.00
    Latency Mean (s):                             214.50
    Latency Median (s):                           214.50
    Latency P90 (s):                              214.50
    Latency P95 (s):                              214.50
    Latency P99 (s):                              214.50
    --------------------------------------------------
    Peak Memory Max (MB):                         46692.00
    Peak Memory Mean (MB):                        46692.00
    Peak Memory Median (MB):                      46692.00
    ------------------------------------------------------------
    ```
  </Tab>
</Tabs>

#### 5.1.2 Generate videos with high concurrency

<Tabs>
  <Tab title="NVIDIA B200">
    **Server Command**:

    ```shell Command
    SGLANG_CACHE_DIT_ENABLED=true \
    SGLANG_CACHE_DIT_FN=2 \
    SGLANG_CACHE_DIT_BN=1 \
    SGLANG_CACHE_DIT_WARMUP=4 \
    SGLANG_CACHE_DIT_RDT=0.4 \
    SGLANG_CACHE_DIT_MC=4 \
    SGLANG_CACHE_DIT_TAYLORSEER=true \
    SGLANG_CACHE_DIT_TS_ORDER=2 \
    sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
    ```

    **Benchmark Command**:

    ```shell Command
    python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
        --backend sglang-video --dataset vbench --task t2v --num-prompts 20 --max-concurrency 20
    ```

    **Result**:

    ```text Output
    ================= Serving Benchmark Result =================
    Backend:                                 sglang-video
    Model:                                   Wan-AI/Wan2.2-T2V-A14B-Diffusers
    Dataset:                                 vbench
    Task:                                    t2v
    --------------------------------------------------
    Benchmark duration (s):                  5163.21
    Request rate:                            inf
    Max request concurrency:                 20
    Successful requests:                     20/20
    --------------------------------------------------
    Request throughput (req/s):              0.00
    Latency Mean (s):                        2739.7695
    Latency Median (s):                      2742.0673
    Latency P99 (s):                         5121.6331
    --------------------------------------------------
    Peak Memory Max (MB):                    72523.56
    Peak Memory Mean (MB):                   70253.34
    Peak Memory Median (MB):                 70824.46

    ============================================================
    ```
  </Tab>

  <Tab title="Ascend A3">
    **Server Command**:

    ```shell Command
    #One A3 card has 2 npu chips. Using four A3 cards in benchmarking
    SGLANG_CACHE_DIT_FN=2 \
    SGLANG_CACHE_DIT_BN=1 \
    SGLANG_CACHE_DIT_WARMUP=4 \
    SGLANG_CACHE_DIT_RDT=0.4 \
    SGLANG_CACHE_DIT_MC=4 \
    SGLANG_CACHE_DIT_TAYLORSEER=true \
    SGLANG_CACHE_DIT_TS_ORDER=2 \
    SGLANG_CACHE_DIT_ENABLED=true sglang serve \
      --model-path /models/Wan-AI/Wan2.2-T2V-A14B-Diffusers/ \
      --tp-size 2 \
      --sp-degree 4 \
      --num-gpus 8 \
      --attention-backend laser_attn
    ```

    **Benchmark Command**:

    ```shell Command
    python -m sglang.multimodal_gen.benchmarks.bench_serving \
      --dataset vbench \
      --task text-to-video \
      --num-prompts 20 \
      --max-concurrency 20
    ```

    **Result**:

    ```text Output
    ================= Serving Benchmark Result =================
    Task:                                         text-to-video
    Model:                                        Wan-AI/Wan2.2-T2V-A14B-Diffusers/
    Dataset:                                      vbench
    --------------------------------------------------
    Benchmark duration (s):                       4384.65
    Request rate:                                 inf
    Max request concurrency:                      20
    Successful requests:                          20/20
    Completed outputs:                            20
    Outputs per prompt:                           1
    --------------------------------------------------
    Request throughput (req/s):                   0.00
    Output throughput (outputs/s):                0.00
    Latency Mean (s):                             2304.17
    Latency Median (s):                           2297.69
    Latency P90 (s):                              3972.32
    Latency P95 (s):                              4178.99
    Latency P99 (s):                              4343.52
    --------------------------------------------------
    Peak Memory Max (MB):                         46692.00
    Peak Memory Mean (MB):                        46691.90
    Peak Memory Median (MB):                      46692.00
    ------------------------------------------------------------
    ```
  </Tab>
</Tabs>
